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Chronic diseases are increasing incessantly, and more efforts are needed in order to develop effective organisational models in primary health care, which may address the challenges posed by the consequent multimorbidity. The aim of this study was to assess and map methods, interventions and outcomes investigated over the last decade regarding the effectiveness of chronic care organisational models in primary care settings. We conducted a scoping review including systematic reviews, clinical trials, and observational studies, published from 2010 to 2020, that evaluated the effectiveness of organisational models for chronic conditions in primary care settings, including home care, community, and general practice. We included 67 international studies out of the 6,540 retrieved studies. The prevalent study design was the observational design (25 studies, 37.3%), and 62 studies (92.5%) were conducted on the adult population. Four main models emerged, called complex integrated care models. These included models grounded on the Chronic Care Model framework and similar, case or care management, and models centred on involvement of pharmacists or community health workers. Across the organisational models, self‐management support and multidisciplinary teams were the most common components. Clinical outcomes have been investigated the most, while caregiver outcomes have been detected in the minority of cases. Almost one‐third of the included studies reported only significant effects in the outcomes. No sufficient data were available to determine the most effective models of care. However, more complex models seem to lead to better outcomes. In conclusion, in the development of more comprehensive organisational models to manage chronic conditions in primary health care, more efforts are needed on the paediatric population, on the inclusion of caregiver outcomes in the effectiveness evaluation of organisational models and on the involvement of social community resources. As regarding the studies investigating organisational models, more detailed descriptions should be provided with regard to interventions, and the training, roles and responsibilities of health and lay figures in delivering care. [ABSTRACT FROM AUTHOR]

. Frequency and reasons for missed nursing care: a cross-sectional study in Veneto Region hospitals., Introduction: Missed nursing care (MNC) encompasses clinical, relational, and emotional aspects of care that are neglected, delayed, or incomplete as performed by nurses., Aims: 1. To describe the frequency of MNC across various clinical settings within hospitals in the Veneto Region; 2. to identify the underlying motivations for MNC; 3. to analyze the relationship between the characteristics of nurses, organizational factors, and the perceived frequency of MNC., Methods: This cross-sectional exploratory study involved 12 healthcare organizations in the Veneto region. A total of 2,179 nurses and 159 coordinators participated, completing the Unfinished Nursing Care Survey questionnaire. Data was collected on the individual characteristics of the nurses and the organizations involved., Results: The least missed care activities included patient monitoring and surveillance, while the most missed were active/passive mobilization, supervision of support staff, and oral care. The primary reasons for MNC were related to insufficient human resources, unpredictability of workflow, and communication difficulties. Senior nurses were more likely to report MNC (OR 1.026; 95% CI 0.567-0.963), whereas those more satisfied with their role in the unit (OR 0.691; 95% CI 0.585-0.817) and those working in spoke hospitals (OR 0.739; 95% CI 0.567-0.963) were less likely to perceive missed nursing care., Conclusions: The study emphasizes that, generally, nurses perceive that they infrequently miss, omit, or delay care activities, particularly those related to patient monitoring and surveillance.

. Identifying population needs: methods of analysis and stratification. In this article, examples of population stratification models used at the national level to identify different levels of needs and interventions are reported. Most models are based primarily on health data, diseases, clinical complexity, use of health services, hospitalizations, emergency room access, pharmaceutical prescriptions, and exemption codes. The limitations of these models relate to the availability and integration of data, as well as generalizability in different contexts. Moreover, to address the difficulty of implementing effective local interventions, the co-production or integration of social and health services is strongly suggested. Some survey techniques are presented to detect the needs, expectations, and resources of specific communities or populations.